Bellman Mapping

Bellman mapping, a core component of reinforcement learning, is being actively refined to improve its efficiency and robustness in real-world applications. Current research focuses on developing nonparametric Bellman mappings within reproducing kernel Hilbert spaces (RKHSs), leveraging their powerful approximation capabilities and avoiding assumptions about data distributions. These advancements, often incorporating techniques like random Fourier features to manage dimensionality, are primarily applied to adaptive filtering problems, particularly in handling outliers. This work demonstrates the potential of advanced Bellman mappings to create more adaptable and resilient algorithms for various online learning tasks.